class_docstr: Abstract method that always selects the base classifier that predicts
  the correct label if such classifier exists. This method is often used to
  measure the upper-limit performance that can be achieved by a dynamic
  classifier selection technique. It is used as a benchmark by several
  dynamic selection algorithms.
  
  Parameters
  ----------
  pool_classifiers : list of classifiers (Default = None)
      The generated_pool of classifiers trained for the corresponding
      classification problem. Each base classifiers should support the method
      "predict". If None, then the pool of classifiers is a bagging
      classifier.
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  n_jobs : int, default=-1
      The number of parallel jobs to run. None means 1 unless in
      a joblib.parallel_backend context. -1 means using all processors.
      Doesn’t affect fit method.
  
  References
  ----------
  Kuncheva, Ludmila I. "A theoretical study on six classifier fusion
  strategies." IEEE Transactions on Pattern Analysis & Machine Intelligence,
  (2002): 281-286.
  
  R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier
  selection: Recent advances and perspectives,”
  Information Fusion, vol. 41, pp. 195 – 216, 2018.
docstr: Return the mean accuracy on the given test data and labels.
  
  Parameters
  ----------
  X : array of shape (n_samples, n_features)
      The data to be classified.
  
  y : array of shape (n_samples)
      Class labels of each sample in X.
  
  sample_weight : array-like of shape (n_samples,), default=None
      Sample weights.
  
  Returns
  -------
  accuracy : float
             Classification accuracy of the Oracle model.
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code: def score(self, X, y, sample_weights=None):
      """Return the mean accuracy on the given test data and labels.
  
      Parameters
      ----------
      X : array of shape (n_samples, n_features)
          The data to be classified.
  
      y : array of shape (n_samples)
          Class labels of each sample in X.
  
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights.
  
      Returns
      -------
      accuracy : float
                 Classification accuracy of the Oracle model.
      """
      from sklearn.metrics import accuracy_score
  
      accuracy = accuracy_score(
          y, self.predict(X, y), sample_weight=sample_weights
      )
      return accuracy
  
file_path: DESlib/deslib/static/oracle.py
imports: import numpy
  from sklearn.utils.validation import check_X_y, check_array
  from deslib.static.base import BaseStaticEnsemble
label_desc: Use image node_id 5 for calling the Oracle obj's underlying member method code with example usage: obj.score(X, y, sample_weights) and returns: accuracy